Thursday, January 18, 2018

There are several popular approaches to the goal of finding generalities in ecology. One is essentially top down, searching for generalities across ecological patterns in multiple places and at multiple scales and then attempting to understand the underlying mechanisms (e.g. metabolic scaling theory and allometric approaches). Alternatively, the approach can be bottom up. It may consider multiple models or multiple individual mechanisms and find generalities in the patterns or relationships they predict.

A great example of generalities from multiple models is in a recent paper published in PNAS (from Sakavara et al. 2018). It relies on, links together, and adds to, our understanding of community assembly and the effects of competition on the distribution of niches in communities. In particular, it adds additional support to the assertion that both combinations of either highly similar or highly divergent species can coexist, across a wide variety of models.

Work published in 2006 by Scheffer and van Nes played an important early role towards a reconciliation of neutral theory and niche-based approaches. They used a Lotka-Volterra model to highlight that communities could assemble with clusters of coexisting, similar species evenly spaced along a niche axis (Figure 1). Neutrality, or at least near-neutrality, could result even when dynamics were determined by niche differences. [Scheffer, van Nes, and Remi Vergnon also provide a nice commentary on the Sakavara et al. paper found here].

One possibility is that Scheffer and van Nes's results might be due to the specifics of the L-V model rather than representing a general and biologically realistic expectation. Sakavara et al. address this issue using a mechanistic consumer-resource model in "Lumpy species coexistence arises robustly in fluctuating resource environments". Under this model, originally from Tilman's classic work with algae, coexistence is limited by the number of resources that limit a species' growth. For 2 species, for example, 2 resources must be present that limit species growth, and further the species must experience a tradeoff in their competitive abilities for the 2 resources. Coexistence can occur when each species is limited more by the resource on which it is most competitive (Figure 2). Such a model– in which resources limit coexistence—leads to an expectation that communities will assemble to maximize the dissimilarity of species.

Fig 2. From Sakavara et al. (2017).

Such a result occurs when resources are provided constantly, but in reality the rates of resource supply may well be cyclical or unpredictable. Will community assembly be similar (resulting in patterns of limiting similarity) when resources are variable in their supply? Or will clumps of similar species be able to coexist? Sakavara et al. considered this question using consumer-resource models of competition, where there are two fluctuating limiting resources. They simulated the dynamics of 300 competing species, which were assigned different trait values along a trait gradient. Here the traits were the half-saturation coefficients for the 2 limiting resources: these were related via a tradeoff between the half saturation constants for each resource.

What they found is strikingly similar to the results from Scheffer and van Nes and dissimilar to the the results that emerge when resources are constant. Clumps of coexisting species emerged along the trait axis. When resource fluctuations occurred rapidly, only fairly specialized species survived in these clumps (R* values that were high for either resource 1, or resource 2, rather than intermediate). But when fluctuations were less frequent, clusters of species also survived at intermediate points along the trait axis. However, in all cases the community organized into clumps composed of very similar species that were coexisting (see Figure 3). It appears that this occurs because the fluctuating resources result in the system having non-stationary conditions. That is, similar sets of species can coexist because the system varies between those species' requirements for persistence and growth.

Fig. 3. "Lumpy species coexistence". The y-axis shows the trait value (here, the R*) of species present under 360 day periodicity of resource supply.

Using many of the dominant models of competition in ecology, it is clearly possible to explain the coexistence of both similar or dissimilar species. This is true across approaches from the Lotka-Volterra results of Scheffer and van Nes, to Tilman's R* resource competition, to Chessonian coexistence (2000). It provides a unifying expectation upon which further research can build. Perhaps the paradox of the planktons is not really a paradox anymore?

Thursday, January 4, 2018

I started off the New Year with a much-needed bookmark reorganization and deletion, which also gave me a chance to re-read some of the links I've held onto (sometimes for years). There's an ever-increasing amount of useful content on the internet, but these have proven some of the most helpful, concrete, and lasting guides for navigating a scientific life.

I thought I'd collate the list here with the hope others might find some of these useful.

I think most of us took different and often interesting routes to science (for example, I grew up in an evangelical Christian family, took a number of years to finally start my undergrad, and had no particular knowledge of ecology when I started my BSc. I wanted to be a vet, but now I'm an ecologist. Close enough :) ) and so I like to hear the many different routes by which scientists found science (SEAS).

Overcoming imposter syndrome - there are many websites devoted to the topic, but this one provides particularly concrete steps to overcoming this common problem.

Mentoring plans - this is super-useful whether you are faculty or a student. In a lot of places there may not be a tradition of having written plans and this provides examples of how to put them into practice. (Meg Duffy, Dynamic Ecology)

Monday, December 18, 2017

Here is this year's card, with best wishes from both of us at the EEB & Flow!

It gets a little harder every year to figure these out. R's plotting capabilities improve every year, but usually via specialized packages. I've tried more and more to use as few additional packages beyond base, and to produce a script that is hopefully compatible across platforms.

For best performance, users must install the 'deldir' package and the 'RCurl' package. This lets you download the necessary data file with as little effort as possible.

If you have trouble accessing the file via the URL, you can just download the data file from Github directly, making sure to load the file into R using the hashed out code in Lines6-7.

It turns out that Price was wrong about single-author extinction, although he hadn't misread the trends. Since the 1970s, the proportion of single-authored papers at the journal have declined to less than 4% and the mean number of authors has risen to more than 5 (Figure 1).

Fig 1.

It's also notable that single-authored papers are cited significantly less often and are 2.5x less likely to be accepted (!). (If that statistic doesn't make you want to gather some coauthors, nothing will). These trends agree with others reported in the literature.

The authors hypothesize that a number of factors drive this result. Ecology has gotten 'bigger' in many ways - analyses are less likely to focus on single populations or species and more likely to be replicated through space and or time. This increased breadth requires more students or assistants to aid with experimental or field work, or collaborations with other labs to bring such data together. Similarly, ecological data collection and analyses often require multiple types of specialized knowledge, whether statistical, mathematical, technological, or systems-based. And by relying on multiple researchers to play specialized roles, the overall quality of a manuscript might be higher (as compared to a jack-of-all trades). The authors also suggest that factors including the growing number of ecologists, the more international scope of many research activities, and more democratic approaches to authorship have increased the mean number of co-authors.

What makes these results particularly interesting is that I think there is still something of a cachet for the sole-authored paper. The conceit is that writing a sole authored paper means that you have a fully realized research plan, and you're accomplished enough to bring it to fruition by yourself. But these stats at least seem to suggest that you're better off with a few friends :)

Wednesday, November 22, 2017

I spend a lot of time thinking about the related topics of conservation, biodiversity, and evolution, so I was interested to see an editorial in the Washington Post on precisely those issues. The article, "We don’t need to save endangered species. Extinction is part of evolution" by Alex Pyron, presents a misrepresentative and potentially harmful position about the future of the earth's biota.

Pyron begins by stating that "Evolution loves death." Selection necessarily means the success of one variant at the expense of others, and today's living creatures are the survivors of an ongoing battle for existence. Extinction is not a modern phenomenon by any means. There have been five mass extinctions, including the glaciation of Gondwana and the impact of an asteroid that lead to the loss of the dinosaurs.

But the 6th great extinction (the Anthropocene extinction - the one we are currently living in) shares little in common with these past events. This is the only extinction that a single species (humans) are primarily responsible for, through activities from habitat conversion or degradation, land fragmentation, warming climate, ocean acidification, and human consumption of natural resources. In this context, Pyron's argument seems to be that we ought to retain an anthropocentric viewpoint of conservation as well. That is, we are simply selecting for species that can survive in our wake, and we should feel concern only for those species that we need.

"But the impulse to conserve for conservation’s sake has taken on an unthinking, unsupported, unnecessary urgency. Extinction is the engine of evolution, the mechanism by which natural selection prunes the poorly adapted and allows the hardiest to flourish. Species constantly go extinct, and every species that is alive today will one day follow suit. There is no such thing as an “endangered species,” except for all species. The only reason we should conserve biodiversity is for ourselves, to create a stable future for human beings. Yes, we have altered the environment and, in doing so, hurt other species. This seems artificial because we, unlike other life forms, use sentience and agriculture and industry. But we are a part of the biosphere just like every other creature, and our actions are just as volitional, their consequences just as natural. Conserving a species we have helped to kill off, but on which we are not directly dependent, serves to discharge our own guilt, but little else."

This is hardly an original viewpoint (hastening to the Bible's 'Then God said, “Let Us make man in Our image, according to Our likeness; let them have dominion over the fish of the sea, over the birds of the air, and over the cattle, over all the earth and over every creeping thing that creeps on the earth.'). But it is a short-sighted one. Ignoring more philosophical arguments about the intrinsic value of all species, the arguments presented are problematic and incomplete, and the potential cost could be huge.

Pyron notes that we may be over-estimating the loss of species:

"According to some studies, it’s not even clear that biodiversity is suffering. The authors of another recent National Academy of Sciences paper point out that species richness has shown no net decline among plants over 100 years across 16,000 sites examined around the world."

The study cited by Pyron here does not support the assertion that biodiversity is fine. In fact, Vellend et al (2013) show that at local scales, plant diversity (i.e., the number of plant species; species number being only way of characterizing biodiversity) has been stable. This isn't the same as saying species are not being lost at a global scale. In a follow-up piece (Vellend et al. 2016), the same author notes that at the global scale, "Nonetheless, if we take 142 and 592 as somewhere in the ballpark of extinctions that have occurred between 1600 and 2016, we get extinction rates of 0.98–4.1, 1–2 orders of magnitude higher than the background rate." Outside of plants, Pimm et al. (2014)'s comprehensive review of extinctions in birds, amphibians, and mammals show extinction rates have at least doubled since 1900. These are rates much higher than considered 'natural'. Even when no extinctions have occurred yet, populations are declining rapidly (Ceballos and Ehrlich 2014, Ceballos et al 2017).

An anthropocentric approach also requires complete understanding and control of our environment. Preventing the loss of the species we need or the ecosystems we rely on is not straightforward (as seen by the rarity with which species become 'non-endangered'). Humans are still under-informed about ecosystem services and goods, and what biotic and abiotic interactions are essential to maintain them. The existence of IPBES is a good indicator of how essential and lacking this information is. To confidently state that "Conserving a species we have helped to kill off, but on which we are not directly dependent, serves to discharge our own guilt, but little else" ignores the indirect linkages that might matter, and our lack of knowledge of them.

Further, the philosophy that humans will survive somehow, in the face of losses of biodiversity and changing planetary climate is probably mostly true for the richest members of the planet. Elsewhere, food shortage associated with climate change (eg.) and water shortages (eg.) already threaten individuals in less wealthy countries.

Ironically, Pyron suggests that all we need to make this reality is "moderation".

"The solution is simple: moderation. While we should feel no remorse about altering our environment, there is no need to clear-cut forests for McMansions on 15-acre plots of crabgrass-blanketed land. We should save whatever species and habitats can be easily rescued (once-endangered creatures such as bald eagles and peregrine falcons now flourish), refrain from polluting waterways, limit consumption of fossil fuels and rely more on low-impact renewable-energy sources....We cannot thrive without crops or pollinators, or along coastlines as sea levels rise and as storms and flooding intensify."

But the anthropocentric view of the world that he presents is the opposite of moderation. It favours only humans. In many ways it's the other extreme of the Half-Earth proposal that suggests we set aside half the planet made free of humans. Having been told we don't need to value species beyond our current needs and interests assumes that we will capably and correctly identify those needs and goals, including for time frames beyond our own myopic lifespans. This uncertainty means that a human-centric view may be just as harmful to humans as approaches that ascribe value for biodiversity more value. And humans have proven willing and capable of taking much broader and more effective actions, that accommodate both humans and other organisms. (As FDR said and did: "We have fallen heirs to the most glorious heritage a people ever received, and each one must do his part if we wish to show that the nation is worthy of its good fortune.")

It's frustrating to see this kind of description of biodiversity as though the earth is simply a plus-minus ledger of species – a few lost here, a few gained there.

A conservation baseline is meant to capture an idealized Eden is of course unreasonable. But Pyron's view looks like Hell. ("If this means fewer dazzling species, fewer unspoiled forests, less untamed wilderness, so be it. They will return in time.")

Edit (Nov. 24): the TL:DR is that a) I thought the author cherrypicked the ecological literature and downplayed what we know about the loss of biodiversity and the complex/negative effects of human actions; b) if the argument is that we should think about biodiversity over timescales of millions of years, humans don't matter anyways; c) if we do care about humans, utility values of biodiversity are an acceptable focus of conservation. But it would be misguided to think that we have a perfect understanding of how ecosystems work or a perfect ability to forecast our impacts. For reasons of uncertainty, sampling effects and option value argue that we preserve as much diversity as we can;d) Non-economic utility values (aesthetic, cultural values) are a good argument for conservation too. Most of us want to leave our children a beautiful planet that is full of life.

Thursday, November 16, 2017

The relationship between biodiversity and ecosystem functioning is so frequently discussed in the ecological literature that it has its own ubiquitous acronym (BEF). The literature has moved from early discussions and disagreements about mechanism, experimental design, and species richness to ask how different components of biodiversity might contribute differentially to functioning. The search is for mechanisms which hopefully will lend predictability to biodiversity-function relationships. One approach is to independently manipulate different facets of biodiversity – whether species, phylogenetic, trait-based, or genetic diversity – to help disentangle the relative contribution of each.

A new paper extends this question by considering how within-species diversity – including genotypic richness, genetic differences, and trait differences – contribute to functioning. Abbott et al. (2017, Ecology) use a field-based eelgrass system to explore how independent manipulations of genotypic richness and genetic relatedness affected biomass production and invertebrate community richness. They collected 41 unique genotypes of eelgrass (Zostera marina), and used 11 species-relevant loci to determine the relatedness of each genotype pair. The authors also measured 17 traits relevant to performance including "growth rate, nutrient uptake, photosynthetic efficiency, phenolic content, susceptibility to herbivores, and detrital production ".

Each of these of these measures are inter-related, but not necessarily in clear, predictable fashions. Genotypes likely differ functionally, but some traits and some genotypes will vary more than others. Genetic distances or relatedness between species similarly may be proxies for trait differences, but this depends on the underlying evolutionary processes. The relationship between any of these measures and functions such as biomass production are no doubt varied and dependent on the mechanism.

The authors established plots with two levels of genotypic richness, either 2 genotypes or 6 genotypes, where genotypes varied among the 41 available. Fully crossed with the genotypic richness treatment was a genetic relatedness treatment: genotypes were either more closely related than a random selection, less closely related, or as closely related as random. At the end of the experiment, above and belowground biomass were collected, and epifaunal invertebrates were collected, and modelled as a component of the biodiversity components.

Because of early die-offs in many plots, planted genotype richness differed from final richness greatly (very few plots had 6 genotypes remaining, for example). For that reason, final diversity measures were used in the models. The relationship between aboveground biomass or belowground biomass and biodiversity were similar: both genotypic richness and genotypic evenness were positively related to total final biomass, but genetic relatedness was negatively correlated. That is, plots with more related genotypes were less productive. Other variables such as trait diversity was not as important, and in fact they did not find any relationship between trait differences and degree of genetic relatedness between genotypes. Since relatedness seemed unrelated to functional similarities, between genotypes, the authors suggested that possibly that reduced biomass among related genotypes is due to self-recognition mechanisms. Most interestingly, the best predictors of invertebrate grazer diversity were opposite - – the best predictor was trait diversity, not genotypic richness or genetic relatedness.

Even in this case, where Abbott et al. were able to separate different diversity components experimentally, it's clear that simplistic predictions as to how they contribute to functioning are insufficient. The contributions of genotypic versus trait diversity were not strongly related. Further, trait diversity performed best on the function for which genotypic diversity performed worst. Understanding what this means is difficult - are the traits relevant for understanding intraspecific interactions (resource usage, etc) so incredibly different from those relevant for interspecific interactions with herbivores? Are the 17 traits too few to capture all differences, or too many irrelevant traits? Do we expect different biodiversity facets have unique independent effects on ecosystem functions, or does the need to consider multiple facets simply mean we have an imperfect understanding of how different facets are related?

Friday, October 27, 2017

In a time when most news about human impacts on the Earth's biodiversity seems to be negative, a new paper in Nature provides a glint of good news about our ability to change the current trend of loss. Encouraging new conservation efforts and funding may be contingent on providing evidence that such efforts will actually be effective.

The new report from Waldron et al. (2017) provides evidence for a predictable relationship between conservation spending and reduction of biodiversity loss. They focused on signatory countries of the Earth Summit's Convention on Biological Diversity and Sustainable Development Goals, and developed a pressures-and-conservation-impact’ (PACI) model to predict how biodiversity loss changed in these countries between 1996-2008. Improvements were driven by conservation spending (relativized to reflect differences in buying power between nations) and were counteracted by GDP growth and agricultural expansion.

Using this model, the authors could predict how the conservation investments made in these nations had affected their loss of biodiversity, as compared to the scenario in which no investment had been made. Amazingly, the median loss of biodiversity per nation was 29% lower than would otherwise have been expected. Over 1996-2008, seven countries even had net biodiversity improvements: Mauritius, Seychelles, Fiji, Samoa, Tonga, Poland and Ukraine.

They discuss a number of interactions among model terms that capture greater socio-economic complexity - for example, the impacts of GDP growth on biodiversity loss are lower when a country's base GDP is very low. Such large scale studies naturally face data limitations - here, they use mammal and bird Red List status changes to develop a quantitative measure of biodiversity loss. Other taxa presumably show similar trends, but we lack the data to incorporate them at this moment.

Hopefully by demonstrating this cost-benefit analysis for conservation actions, Waldron et al. (2017) encourage future 'investors' as to the payoff of spending on conservation.

Friday, October 6, 2017

In case you missed it, a new paper in Royal Society Open Science from seven popular ecology blogs discusses the highlights and values of science community blogging. It provides some insights into the motivations behind posting and the reach and impacts that result. It's a must-read if you've considered or already have a blog about science.

It was nice to see how universal the 'pros' of blogging seem to be – the things I most appreciate about contributing to a blog are pretty similar to the things the authors here reported on too. According to the archives, I've been posting here since 2010, when I was a pretty naïve PhD student interacting with the ecological literature for the first time. I had a degree of enthusiasm and wonder upon interacting with ideas for the first time that I miss, actually. I just started a faculty job this fall, and I think that the blog allowed me to explore and experiment with ideas as I figured out where I was going as a scientist (which is still an ongoing process).

As Saunders et al. note, one of the other major upsides to blogging is the extent to which it produces networking and connections with colleagues. In a pretty crowded job market, I think it probably helped me, although only as a complement to the usual suspects (publications, 'fit', research plans, interviewing skills). Saunders et al. also mentioned blogging as relevant to NSF's Broader Impacts section, which I actually hadn't considered. Beyond that, the greatest benefit by far for me is that forcing oneself to post regularly and publicly is amazing practice for writing about science.

Despite these positives, I don't necessarily think a science blog is for everyone and there are definitely things to consider before jumping in to it. It can be hard to justify posting on a blog when your to-do list overflows, and not everyone will –understandably- think that's a good use of their time. There is a time commitment and degree of prioritisation required that is difficult. This is one reason that having co-bloggers can be a lifesaver. It is also true that while writing a blog is great practice, it probably selects for people able to write quickly (and perhaps without perfectionistic tendencies).

When students ask me about blogging, they often hint at concerns in sharing their ideas and writing. It can be really difficult to put your ideas and writing out there (why invite more judgement and criticism?) and this is can feedback with imposter syndrome (speaking from my own experience). For a long time, minorities, women, students have been under-represented in ecology blogs, and I think this may be a contributor to that. It's nice to see more women blogging about these days, and hopefully there is a positive feedback from increasing the visibility of under-represented groups.

In any case, this paper was especially timely for me, because I've been re-evaluating over the past few months about whether to keep blogging or not, and this provided a reminder of the positive impacts that are easy to overlook.

Tuesday, September 26, 2017

The last post here mused on the connection between (but also, distinctness of) the scientific goals of "understanding" and "prediction". An additional goal of science is "description", the attempt to define and classify phenomenon. Much as understanding and prediction are distinct but interconnected, it can be difficult to separate research activities between description and understanding. Descriptive research is frequently considered preliminary or incomplete on its own, meant to be an initial step prior to further analysis. (On the other hand, the decline of more descriptive approaches such as natural history is often bemoaned). With that in mind, it was interesting to see several recent papers in high-impact journals that rely primarily on descriptive methods (especially ordinations) to provide generalizations. It's fairly uncommon to see ordination plots as the key figure in journals like Nature or The American Naturalist, and it opens up the question of 'when do descriptive methods exceed description and provide new insights & understanding?'

For example, Diaz et al.'s 2016 Nature paper took advantage of a massive database of trait data (from ~46000 species) to explore the inter-relationships between 6 ecologically relevant plant traits. The resulting PCA plot (figure below) illustrates, across many species, that well-known tradeoffs between a) organ size and scaling and b) the tissue economic spectrum appear fairly universal. Variation in plant form and function may be huge, but the Diaz et al. ordination highlights that it still is relatively constrained, and that many strategies (trait combinations) are apparently untenable.

From Diaz et al. 2016.

Similarly, a new paper in The American Naturalist relies on ordination methods to try to identify 'a periodic table of niches' of lizards (Winemiller et al. 2015 first presented this idea) – i.e. a classification framework capturing the minimal, clarifying set of universal positions taken by a set of taxa. Using the data and expert knowledge on lizard species collected over a lifetime of research by E. Pianka and L. Vitt, Pianka et al. (2017) first determine the most important life history axes -- habitat, diet, life history, metabolism, and defense attributes. They use PCoA to calculate the position of each of 134 species in terms of each of the 5 life history axes, and then combined the separate axes into a single ordination (see figure below). This ordination highlights that niche convergence (distant relatives occupy very similar niche space) and niche conservation (close relatives occupy very similar niche space) are both common outcomes of evolution. (For more discussion, this piece from Jonathon Losos is a great). Their results are less clarifying than those in Diaz et al. (2016): a key reason may simply be the smaller size of Pianka et al.'s data set and its greater reliance on descriptive (rather than quantitative) traits.

From Winemiller et al. 2017

Finally, a new TREE paper from Daru et al. (In press) attempts to identify some of the processes underlying the formation of regional assemblages (what they call phylogenetic regionalization, e.g. distinct phylogenetically delimited biogeographic units). They similarly rely on ordinations to take measurements of phylogenetic turnover and then identify clusters of phylogenetically similar sites. Daru et al.'s paper is slightly different, in that rather than presenting insights from descriptive methods, it provides a descriptive method that they feel will lead to such insights.

Part of this blip of descriptive results and methods may be related to a general return to the concept of multidimensional or hypervolume niche (e.g. 1, 2). Models are much more difficult in this context and so description is a reasonable starting point. In addition, the most useful descriptive approaches are like those seen here - where new data or a lot of data (or new techniques that can transform existing data) - are available. In these cases, they provide a route to identifying generalization. (This also leads to an interesting question – are these kind of analyses simply brute force solutions to generalization? Or do descriptive results sometimes exceed the sum of their individual data points?)

Thursday, September 7, 2017

When we learn about the scientific method, the focus is usually on hypothesis testing and deductive reasoning. Less time is spent on considering the various the outcomes of scientific research, specifically: description, understanding, and prediction. Description involves parsimoniously capturing data structure, and may use statistical methods such as PCA to reduce data complexity and identify important axes of variation. Understanding involves the explanation of phenomenon by identifying causal relationships (such as via parameter estimation in models). Finally, prediction involves estimating the values of new or future observations. Naturally, some approaches in ecology orient more closely toward one of these outcomes than others and some areas of research historically have valued one outcome over others. For example, applied approaches such as fisheries population models emphasize predictive accuracy (but even there, there are worries about limits on prediction). On the other hand, studies of biotic interactions or trophic structure typically emphasize identifying causal relationships. The focus in different subdisciplines no doubt owes something to culture and historical priority effects.

In various ways these outcomes feedback on each other – description can inform explanatory models, and explanatory models can be evaluated based on their predictions. In a recent paper in Oikos, Houlahan et al. discuss the tendency of many ecological fields to under-emphasize predictive approaches and instead focus on explanatory statistical models. They note that prediction is rarely at the centre of ecological research and that this may be limiting ecological progress. There are lots of interesting questions that ecologists should be asking, including what are the predictive horizons (spatial and temporal scales) over which predictive accuracy decays? Currently, we don't even know what a typical upper limit on model predictive ability is in ecology.

Although the authors argue for the primacy of prediction ["Prediction is the only way to demonstrate scientific understanding", and "any potentially useful model must make predictions about some unknown state of the natural world"], I think there is some nuance to be gained by recognizing that understanding and prediction are separate outcomes and that their relationship is not always straightforward (for a thorough discussion see Shmueli 2010). Ideally, a mutually informative feedback between explanation and prediction should exist, but it is also true that prediction can be useful and worthy for reasons that are not dependent on explanation and vice versa. Further, to understand why and where prediction is limited or difficult, and what is required to correct this, it is useful to consider it separately from explanation.

Understanding/explanation can be valuable and inspire further research, even if prediction is impossible. The goal of explanatory models is to have the model [e.g., f(x)] match as closely as possible the actual mechanism [F(x)]. A divergence between understanding and prediction can naturally occur when there is a difference between concepts or theoretical constructs and our ability to measure them. In physics, theories explaining phenomenon may arise many years before they can actually be tested (e.g. gravitational waves). Even if useful causal models are available, limitations on prediction can be present: in particle physics, the Heisenberg uncertainty principle identifies limits on the precision at which you can know both the position of a particle and its momentum. In ecology, a major limitation to prediction may simply be data availability. In a similar field (meteorology) in which many processes are important and nonlinearities common, predictions require massive data inputs (frequently collected over near continuous time) and models that can be evaluated only via supercomputers. We rarely collect biotic data at those scales in ecology. We can still gain understanding if predictions are impossible, and hopefully eventually the desire to make predictions will motivate the development of new methods or data collection. In many ecological fields, it might be worth thinking about what can be done in the future to enable predictions, even if they aren't really possible right now.

Approaches that emphasize prediction frequently improve understanding, but this is not necessarily true either. Statistically, understanding can come at the cost of predictive ability. Further, a predictive model may provide accurate predictions, but do so using collinear or synthetic variables that are hard to interpret. For example, a macroecological relationship between temperature and diversity may effectively predict diversity in a new habitat, and yet do little on its own to identify specific mechanisms. Prediction does not require interpretability or explanatory ability, as is clear from papers such as "Model-free forecasting outperforms the correct mechanistic model for simulated and experimental data". So it's worth being wary of the idea that a predictive model is necessarily 'better'.

With this difference between prediction and understanding in mind, it is perhaps easier to understand why ecologists have lagged in prediction. For a long time, statistical approaches used in ecology were biased toward those meant to improve understanding, such as regression models, where parameters estimate the strength and direction of a relationship. This is partially responsible for our obsession with p-values and R^2 terms. What Houlahan et al. do a great job of emphasizing is that by ignoring prediction as a goal, researchers are often limiting their ability confirm their understanding. Predictions that are derived from explanatory models Some approaches in ecology have already moved naturally towards emphasizing prediction, especially SDMs/ecological niche models. They recognized that it was not enough to describe species-environment relationships; testing predictions allowed them to determine how universal and mechanistic these relationships actually were. A number of macroecological models fit nicely with predictive statistical approaches, and could adopt Houlahan’s suggestions quite readily (e.g. reporting measures of predictive ability and testing models on withheld data). But for some approaches, the search for mechanism is so deeply integrated into how they approach science that it will take longer and be more difficult (but not impossible)*. Even for these areas, prediction is a worthy goal, just not necessarily an easy one.

*I was asked for examples of 'unpredictable' areas of ecology. This may be pessimistic, but I think that something like accurately predicting the composition (both species' abundance and identity) of diverse communities at small spatial scales might always be difficult, especially given the temporal dynamics. But I could be wrong!

...if the Simpsons could predict Trump, I suppose there's hope for ecologists too...